If you're old enough to remember the Reagan administration, you remember the politically charged expression "trickle-down economics," which referred to the theory that if you provide benefits and incentives to businesses and the wealthy, those benefits would trickle down to wage earners at lower socioeconomic levels.

In some ways, big data analytics is like trickle-down economics. Only the biggest healthcare providers with the deepest pockets can afford the kind of analytics platforms required to get useful intelligence from tens of thousands of patient records. But in theory, those benefits will trickle down to smaller providers that either don't have the financial support or the large patient populations to do this type of data crunching on their own.

How exactly is this supposed to work? Consider this example: A recent study conducted by Kaiser Permanente looked at the incidence of blood clots among women from two integrated health care programs and two state Medicaid programs, who were taking various oral contraceptive (OC) formulas. Their analysis revealed that one formula in particular, containing drospirenone, increased the threat of blood clots by 77% when compared to the risk in women taking several other OC formulas.

How might such big data analytics benefit the checkout girl at my local supermarket, or my car mechanic and his wife, who live in a small town and may be part of a group practice that can't do this kind of research?

Proponents of big data would say that's what peer-reviewed journals are for. The big guys publish the work in a creditable venue and clinicians in smaller community practices learn from their findings.

[ Is it time to re-engineer your clinical decision support system? See 10 Innovative Clinical Decision Support Programs. ]

The only flaw in that reasoning is the huge lag time between published research and widespread adoption of that research in the trenches -- 5 to 10 years, by some estimates. In some cases, the research never gets put into practice.

And that disconnect between big data analysis and the rest of the healthcare system is only one of many. A panel discussion at last week's Connected Health Symposium in Boston raised several others. Even the session's title suggested controversy: "Big Data Healthcare Analytics: Frontier or Fiction?"

Charlie Baker, president of General Catalyst Partners, a venture capital firm, and former Secretary of Health and Human Services for the Commonwealth of Massachusetts, suggested that the industry put less emphasis on big data and more on fundamental payment reform. To fix what's broken in the healthcare system, Baker says, "I'd start with a reimbursement system that awards technology, procedures, fragmentation... and punishes primary care... There's nothing about big data in the short term that's going to solve that problem."

Baker went on to emphasize what many other stakeholders are shouting about: Finding a way to move from a pay-for-services system to a pay-for-value system has to be our top priority, not big data analysis.

That's not to suggest that such analysis shouldn't play a role in healthcare reform. But all the big data in the world isn't going to give us a substantial ROI if hospitals and individual practitioners continue to be rewarded for the quantity rather than quality of care they provide.

InformationWeek Healthcare brought together eight top IT execs to discuss BYOD, Meaningful Use, accountable care, and other contentious issues. Also in the new, all-digital CIO Roundtable issue: Why use IT systems to help cut medical costs if physicians ignore the cost of the care they provide? (Free with registration.)